We adopt the widely used Inception Score (IS) [1]} and Fréchet Inception Distance (FID) [2]} to quantify the quantitative performance. On the MS COCO dataset, an Inception-v3 network pre-trained on Image-net is used to compute the KL-divergence between the conditional class distribution (generated images) and the marginal class distribution (real images). The presence of a large IS indicates that the generated images are of high quality. The FID computes the Fréchet Distance between the image features distribution of the generated and real-world images. The image features are extracted by the same pre-trained Inception v3 network. A lower FID implies the generated images are closer to the real images. To evaluate the IS and FID scores, 30k images are generated by each model from the test dataset as previous works [3]}, [4]}, [5]}, [6]} reported that the IS metric completely fails in evaluating the synthesized images. Therefore, we only compare the FID on the COCO dataset. On the CUB and Oxford-102 dataset, pre-trained Inception models are fine-tuned on two fine-grained classification tasks [7]}.
